# GLM-5.1 Usage, Cost & Rank | OpenCode Data

> GLM-5.1 ranked #38 by tokens across OpenCode last week, with 0.0% of tokens over the past two months. GLM-5.1 costs $1.40 per 1M input tokens and $4.40 per 1M output tokens.

Strong GLM coding model for agentic engineering, terminals, and repository generation

- Page: https://opencode.ai/data/zhipuai/glm-5-1
- JSON: https://opencode.ai/data/zhipuai/glm-5-1.json
- Model ID: zhipuai/glm-5.1
- Lab: Zhipu
- Updated: 2026-10-02T08:39:37.000Z

## Model facts

| Fact | Value |
| --- | --- |
| Context window | 200K |
| Max output | 131K |
| Knowledge cutoff | - |
| Release date | 2026-04-07 |
| Input modalities | text |
| Output modalities | text |
| Reasoning | Yes |
| Tool calling | Yes |
| Open weights | Yes |
| Weights | [Hugging Face](https://huggingface.co/zai-org/GLM-5.1) |

## Pricing: USD per 1M tokens

| Input | Output | Cached input | Cache write |
| --- | --- | --- | --- |
| $1.40 | $4.40 | $0.26 | $0.00 |

## OpenCode usage: past 2 months

| Metric | Value |
| --- | --- |
| Rank by tokens last week | #38 |
| Tokens | 161B |
| Share of all tokens | 0.0% |
| Change vs previous 2 months | -72% |
| Unique users | 88K |
| Completed sessions | 94,703 |
| Average tokens per session | 1.7M |
| Average cost per session | $0.8507 |
| Total spend | $80,560 |
| Input tokens served from cache | 86% |
| Weekly retention | 53.8% |

## Daily usage: past 2 months

| Date | Tokens | Unique users | Sessions |
| --- | --- | --- | --- |
| 2026-08-08 | 2.4B | 1.8K | 1,142 |
| 2026-08-09 | 2.6B | 1.8K | 765 |
| 2026-08-10 | 2.9B | 2.5K | 1,590 |
| 2026-08-11 | 4.1B | 3.3K | 795 |
| 2026-08-12 | 5.9B | 2.2K | 2 |
| 2026-08-13 | 5.7B | 2.4K | 7 |
| 2026-08-14 | 5B | 2.3K | 5 |
| 2026-08-15 | 3.8B | 2.2K | 3 |
| 2026-08-16 | 3.9B | 2.7K | 2 |
| 2026-08-17 | 6.7B | 4.6K | 18 |
| 2026-08-18 | 6.9B | 3.6K | 1,624 |
| 2026-08-19 | 6.2B | 3.1K | 1,763 |
| 2026-08-20 | 5.3B | 2.8K | 1,644 |
| 2026-08-21 | 4.7B | 3.5K | 1,405 |
| 2026-08-22 | 3.1B | 2.7K | 1,017 |
| 2026-08-23 | 2.8B | 2.3K | 991 |
| 2026-08-24 | 6.8B | 5.4K | 1,630 |
| 2026-08-25 | 5.2B | 3K | 2,075 |
| 2026-08-26 | 4.7B | 2.4K | 1,187 |
| 2026-08-27 | 4.5B | 1.7K | 1,013 |
| 2026-08-28 | 3.9B | 1.7K | 1,132 |
| 2026-08-29 | 2.3B | 1.1K | 906 |
| 2026-08-30 | 2.3B | 966 | 918 |
| 2026-08-31 | 4.1B | 1.7K | 1,251 |
| 2026-09-01 | 3.3B | 1.5K | 1,471 |
| 2026-09-02 | 2.9B | 1.4K | 1,287 |
| 2026-09-03 | 3B | 1.4K | 941 |
| 2026-09-04 | 2.4B | 1.1K | 939 |
| 2026-09-05 | 1.5B | 896 | 829 |
| 2026-09-06 | 1.8B | 974 | 963 |
| 2026-09-07 | 2.9B | 1K | 2,616 |
| 2026-09-08 | 2.9B | 863 | 1,911 |
| 2026-09-09 | 2.1B | 789 | 1,900 |
| 2026-09-10 | 2B | 1.1K | 2,249 |
| 2026-09-11 | 1.8B | 896 | 1,804 |
| 2026-09-12 | 1.6B | 658 | 1,511 |
| 2026-09-13 | 1.5B | 739 | 1,510 |
| 2026-09-14 | 2.5B | 1.1K | 4,275 |
| 2026-09-15 | 2B | 1K | 2,055 |
| 2026-09-16 | 1.9B | 1.1K | 3,572 |
| 2026-09-17 | 1.9B | 1.1K | 3,836 |
| 2026-09-18 | 1.8B | 880 | 4,218 |
| 2026-09-19 | 1.3B | 794 | 5,926 |
| 2026-09-20 | 1.6B | 817 | 5,486 |
| 2026-09-21 | 2.4B | 1K | 4,258 |
| 2026-09-22 | 2.5B | 1K | 3,829 |
| 2026-09-23 | 1.9B | 850 | 3,224 |
| 2026-09-24 | 1.5B | 768 | 2,687 |
| 2026-09-25 | 1.5B | 654 | 2,571 |
| 2026-09-26 | 1.1B | 648 | 2,215 |
| 2026-09-27 | 1.2B | 651 | 2,644 |
| 2026-09-28 | 247M | 274 | 841 |
| 2026-09-29 | 0 | 0 | 0 |
| 2026-09-30 | 0 | 0 | 0 |
| 2026-10-01 | 105K | 12 | 15 |
| 2026-10-02 | 44M | 134 | 235 |

## Top countries: past 2 months

| Rank | Country | Tokens | Share |
| --- | --- | --- | --- |
| 1 | China | 27B | 16.9% |
| 2 | United States | 27B | 16.5% |
| 3 | Brazil | 7.7B | 4.8% |
| 4 | Germany | 6.6B | 4.1% |
| 5 | India | 5.9B | 3.7% |
| 6 | Indonesia | 5.7B | 3.5% |
| 7 | Spain | 4.7B | 2.9% |
| 8 | United Kingdom | 4.5B | 2.8% |
| 9 | Mexico | 4.1B | 2.6% |
| 10 | France | 4.1B | 2.6% |
| 11 | Russia | 3.9B | 2.4% |
| 12 | Singapore | 3.1B | 1.9% |
| 13 | Argentina | 3B | 1.9% |
| 14 | Japan | 3B | 1.9% |
| 15 | Colombia | 3B | 1.8% |

## Nearby models by tokens last week

| Rank | Model | Lab | Tokens |
| --- | --- | --- | --- |
| 34 | [grok-4.7](https://opencode.ai/data/xai/grok-4-7.md) | xAI | 9.6B |
| 35 | [jev-1.13](https://opencode.ai/data/unknown/jev-1-13.md) | - | 6B |
| 36 | [kimi-k2.6](https://opencode.ai/data/moonshotai/kimi-k2-6.md) | Moonshot | 5.5B |
| 37 | [grok-4.6](https://opencode.ai/data/xai/grok-4-6.md) | xAI | 2.8B |
| 38 | [glm-5.1](https://opencode.ai/data/zhipuai/glm-5-1.md) | Zhipu | 2.5B |
| 39 | [qwen3.6-plus](https://opencode.ai/data/alibaba/qwen3-6-plus.md) | Qwen | 1.9B |
| 40 | [test-novita-dsf4.1](https://opencode.ai/data/deepseek/test-novita-dsf4-1.md) | DeepSeek | 986M |
| 41 | [qwen3.7-max](https://opencode.ai/data/alibaba/qwen3-7-max.md) | Qwen | 920M |
| 42 | [minimax-m2.5](https://opencode.ai/data/minimax/minimax-m2-5.md) | MiniMax | 720M |
| 43 | [gpt-5-nano](https://opencode.ai/data/openai/gpt-5-nano.md) | OpenAI | 289M |

## Benchmarks

| Benchmark | Score | Metric | Source |
| --- | --- | --- | --- |
| Artificial Analysis Coding Agent Index | 52.7 | average pass@1 | https://artificialanalysis.ai/agents/coding-agents |
| SWE-Atlas Codebase QnA | 73.2 | pass@1 | https://artificialanalysis.ai/agents/coding-agents |
| SWE-Bench Pro | 19.8 | pass@1 | https://artificialanalysis.ai/agents/coding-agents |
| Terminal-Bench | 65.1 | pass@1 | https://artificialanalysis.ai/agents/coding-agents |

## Methodology

- Updates: Aggregated every hour. Days and weeks use UTC.
- Tokens: Input, output, reasoning, and cached tokens for each request.
- Users and sessions: Approximate counts of distinct users and OpenCode sessions.
- Cost: Session cost is the average cost per OpenCode session. Token prices are list prices from the OpenCode model catalog.
- Retention: The share of a model's users in one week who use it again the next week.
- Citation: Cite OpenCode Data (opencode.ai/data) with the update time shown at the top of the page.